Instructions to use Jtisch7/bertFinancialSent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jtisch7/bertFinancialSent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jtisch7/bertFinancialSent")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, TF_AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jtisch7/bertFinancialSent") model = TF_AutoModelForSequenceClassification.from_pretrained("Jtisch7/bertFinancialSent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Jtisch7/bertFinancialSent: direct link, hf CLI and curl.
- Browser
- Download file 538 Bytes
-
https://huggingface.co/Jtisch7/bertFinancialSent/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Jtisch7/bertFinancialSent/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Jtisch7/bertFinancialSent/resolve/main/tokenizer_config.json
538 Bytes
| {"do_lower_case": false, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "tokenizer_file": "C:\\Users\\Jonathan/.cache\\huggingface\\transformers\\226a307193a9f4344264cdc76a12988448a25345ba172f2c7421f3b6810fddad.3dab63143af66769bbb35e3811f75f7e16b2320e12b7935e216bd6159ce6d9a6", "name_or_path": "bert-base-cased"} |